Megan Strait

Megan Strait

Loosely based on principles of similarity-attraction, robots intended for social contexts are being designed with increasing human similarity to facilitate their reception by and communication with human interactants. However, the observation of an uncanny valley - the phenomenon in which certain humanlike entities provoke dislike instead of liking - has lead some to caution against this practice. Substantial evidence supports both of these contrasting perspectives on the design of social technologies. Yet, owing to both empirical and theoretical inconsistencies, the relationship between anthropomorphic design and people's liking of the technology remains poorly understood. Here we present three studies which investigate people's explicit ratings of and behavior towards a large sample of real-world robots. The results show a profound "valley effect" on people's \emph{willingness} to interact with humanlike robots, thus highlighting the formidable design challenge the uncanny valley poses for social robotics. In addition to advancing uncanny valley theory, Studies 2 and 3 contribute and validate a novel laboratory task for objectively measuring people's perceptions of humanlike robots.

Carlos Pena-Caballero, Elifaleth Cantu, Jesus Rodriguez et al.

Along with the improvement of radar technologies, Automatic Target Recognition (ATR) using Synthetic Aperture Radar (SAR) and Inverse SAR (ISAR) has come to be an active research area. SAR/ISAR are radar techniques to generate a two-dimensional high-resolution image of a target. Unlike other similar experiments using Convolutional Neural Networks (CNN) to solve this problem, we utilize an unusual approach that leads to better performance and faster training times. Our CNN uses complex values generated by a simulation to train the network; additionally, we utilize a multi-radar approach to increase the accuracy of the training and testing processes, thus resulting in higher accuracies than the other papers working on SAR/ISAR ATR. We generated our dataset with 7 different aircraft models with a radar simulator we developed called RadarPixel; it is a Windows GUI program implemented using Matlab and Java programming, the simulator is capable of accurately replicating a real SAR/ISAR configurations. Our objective is to utilize our multi-radar technique and determine the optimal number of radars needed to detect and classify targets.